Prognostic significance of immunohistochemical markers in non-small cell lung cancer
Bibliographic record
Abstract
7211 Background: The purpose of this study is to use a large patient population to identify immunohistochemical (IHC) biomarkers to enable improved prognostication in patients with non-small cell lung cancer (NSCLC). Methods: A tissue microarray was constructed using duplicate 0.6 mm cores of formalin-fixed paraffin embedded tissue blocks from 609 patients with NSCLC. IHC was used to detect 11 biomarkers including EGFR, HER2, HER3, p53, p63, Bcl-1, Bcl-2, TTF-1, CEA, Ch, and SNP. A clinical database was created prospectively at the time of tissue collection. Survival outcomes were obtained from a Provincial Cancer Registry database. Results: Male to female ratio was 400:209; median age 63yrs (range 35–82); median survival 3.5yrs (mean 5.7). All specimens were reviewed: 243 adenocarcinoma (ACA), 272 squamous cell carcinoma (SCC), 35 large cell carcinoma, 32 non-small cell carcinoma NOS, and 6 other (giant cell carcinoma). 21 patients with other histologies were excluded. Survival data for 535 cases was available. As of June 2005, 429 patients (80%) had died; of these 286 (54%) died of lung cancer, 117 (22%) died of other known causes, and for 26 (5%) the cause of death was not available. Bcl-2 (p = 0.007) was the only biomarker to predict better overall survival (OS). Bcl-2 (p = 0.021) and p63 (p = 0.025) were significant for disease specific survival (DSS) in all NSCLC. Analysis of the subgroups indicated that p63 was significant (p = 0.039) for DSS in squamous cell carcinoma (SCC) but not for adenocarcinoma (ACA) (p = 0.81). Bcl-2 was not significant for DSS in either subgroup (p = 0.28 for SCC, p = 0.112 for ACA). EGFR expression was associated with improved DSS in SCC (p = 0.012) but not for ACA. Co-expression of EGFR-HER3 was more likely in SCC then in ACA (p = 0.033). There was no correlation between outcome and any combination or clustering of biomarkers. Conclusions: The biomarkers p63 and Bcl-2 are predictive of DSS in NSCLC. EGFR expression is predictive of DSS in SCC. Sub-classification of NSCLC by histopathology is important as the relevance of some biomarkers (EGFR) would be lost if pooled. p63, Bcl-2, and EGFR may be used as prognostic markers in patients with NSCLC. Co-expression of EGFR-HER3 is more likely in SCC then in ACA. This may help explain the differential response to EGFR inhibitors in SCC versus ACA. No significant financial relationships to disclose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".